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Table 4 RF prediction results

From: In silico method for systematic analysis of feature importance in microRNA-mRNA interactions

Feature set

Neg_1

Neg_2

 

Optimal parameters

Se

Sp

Optimal parameters

Se

Sp

SEQ

ntreea = 2000, mtryb = 16

0.873

0.828

ntree = 1000, mtry = 66

0.821

0.885

STUR

ntree = 1500, mtry = 16

0.852

0.826

ntree = 1500, mtry = 7

0.807

0.808

POSI

ntree = 1000, mtry = 5

0.947

0.916

ntree = 1000, mtry = 4

0.917

0.949

Total

ntree = 2000, mtry = 6

0.971

0.918

ntree = 500, mtry = 37

0.870

0.922

  1. Cross-validation was used to estimate the predictor performance of SEQ, STRU, POSI sets and the total feature set for two differet negative data sets. Neg_1 comprises all experimental samples and inferred negative samples and Neg_2 comprises all experimental samples and artificial negative samples from miRanda. Sensitivity (Se) was calculated as TP/(TP+FN) and specificity (Sp) as TN/(TN+FP), where TP is the number of true positives, TN is the number of true negatives, FP is the number of false positives and FN is the number of false negatives.
  2. a number of trees to grow.
  3. b number of variables randomly sampled as candidates at each split.